Abstract
MicroRNAs (miRNAs) are pivotal biomarkers for cancer screening. Identifying distinctive expression patterns of miRNAs in specific cancer types can serve as an effective strategy for classification and characterization. However, the development of a minimal signature of miRNAs for accurate cancer classification remains challenging, hindered by the lack of integrated approaches that systematically analyse miRNA expression levels of miRNAs alongside their associated biological pathways. In this study, we present a comprehensive integrative approach that utilizes transcriptomic data from lung, breast, and melanoma cancer cell lines to identify specific expression patterns. By combining bioinformatics, dimensionality reduction techniques, machine learning, and experimental validation, we pinpoint miRNAs linked to critical biological pathways. Our results demonstrate a highly significant differentiation of cancer types, achieving 100 % classification accuracy with minimal training time using a streamlined miRNA signature. Validation of the miRNA profile confirms that each of the three identified miRNAs regulates distinct biological pathways with minimal overlap. This specificity highlights their unique roles in tumour biology and set the stage for further exploration of miRNAs interactions and their contributions to tumourigenesis across diverse cancer types. Our work paves the way for multi-cancer classification, emphasizing the transformative potential of miRNA research in oncology. Beyond advancing the understanding of tumour biology, our step-by-step guide offers a robust tool for a wide range of users to investigate precise diagnostics and promising therapeutic strategies.